Advances in genomic research have shown that each patient has their own unique tumor profile with patient-specific genetic variations. While silencing RNA (siRNA) screening tests can identify which genes drive tumor cell growth, results obtained from these assays have been limited in their clinical translatability because they employ cell lines growing on flat surfaces. Cell lines exhibit extensive chromosomal instability and behave differently depending on the culture conditions. Cellular response to siRNA in these assays is thus influenced by their attachment to the culture surface and cell-cell contact.
Anchorage-independent three-dimensional (3D) growth assays are considered to be the gold-standard for chemosensitivity testing, and leads identified with these assays have high probability of clinical success. These assays utilize different types of matrices, including soft agar, to inhibit cellular attachment and allow for 3D growth of cells. Transformed tumor cells and stem cells, but not normal cells, are capable of growing under these conditions, since they have the innate capability of uncontrollable cell division. For example, normal epithelial cells depend on cell-cell contact and attachment to a physical support for survival and growth.
In their current format, however, anchorage-independent growth assays are not amenable for large-scale screening because they require large numbers of cells and the plating efficiency of human tumor biopsies is very low. Furthermore, current assays are capable of testing only one inhibitor at a time and are tedious, time consuming, and costly since they are not easily amenable to automation.
Moreover, the current systems and methods known in the art for identifying, diagnosing and treating cancer cells are not capable of being used on an individual or a personalized basis for patient-specific therapy. There is a need, therefore, for an individualized or personalized large-scale screening assay system for identification of genes responsible for anchorage-independent cancer cell growth and for identification of cell-specific inhibitors of cancer cell growth to optimize cancer diagnosis and therapy.